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Adaptive Gradient Quantization for Data-Parallel SGD

Oct 23, 2020
Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel Roy, Ali Ramezani-Kebrya

* Accepted at the conference on Neural Information Processing Systems (NeurIPS 2020) 

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A Study of Gradient Variance in Deep Learning

Jul 09, 2020
Fartash Faghri, David Duvenaud, David J. Fleet, Jimmy Ba


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Adversarial Robustness through Regularization: A Second-Order Approach

Apr 04, 2020
Avery Ma, Fartash Faghri, Amir-massoud Farahmand


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NUQSGD: Improved Communication Efficiency for Data-parallel SGD via Nonuniform Quantization

Aug 16, 2019
Ali Ramezani-Kebrya, Fartash Faghri, Daniel M. Roy

* 21 pages, 6 figures 

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Adversarial Spheres

Sep 10, 2018
Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian Goodfellow


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VSE++: Improving Visual-Semantic Embeddings with Hard Negatives

Jul 29, 2018
Fartash Faghri, David J. Fleet, Jamie Ryan Kiros, Sanja Fidler

* Accepted as spotlight presentation at British Machine Vision Conference (BMVC) 2018. Code: https://github.com/fartashf/vsepp 

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Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

Jun 27, 2018
Nicolas Papernot, Fartash Faghri, Nicholas Carlini, Ian Goodfellow, Reuben Feinman, Alexey Kurakin, Cihang Xie, Yash Sharma, Tom Brown, Aurko Roy, Alexander Matyasko, Vahid Behzadan, Karen Hambardzumyan, Zhishuai Zhang, Yi-Lin Juang, Zhi Li, Ryan Sheatsley, Abhibhav Garg, Jonathan Uesato, Willi Gierke, Yinpeng Dong, David Berthelot, Paul Hendricks, Jonas Rauber, Rujun Long, Patrick McDaniel

* Technical report for https://github.com/tensorflow/cleverhans 

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Adversarial Manipulation of Deep Representations

Mar 04, 2016
Sara Sabour, Yanshuai Cao, Fartash Faghri, David J. Fleet

* Accepted as a conference paper at ICLR 2016 

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